With the increased ease of creating and transmitting electronic document images, it has become common for document images to be maintained in database systems that include automated document insertion and retrieval utilities. Consequently, it has become increasingly important to be able to efficiently and reliably determine whether a duplicate of a document submitted for insertion is already present in a database. Otherwise, duplicate documents will be stored in the database, needlessly consuming precious storage space. Determining whether a database contains a duplicate of a document is referred to as document matching.
In currently available image-content based retrieval systems, color, texture and shape features are frequently used for document matching. Matching document images that are mostly bitonal and similar in shape and texture poses different problems.
A common document matching technique is to perform optical character recognition (OCR) followed by a text based search. Another approach is to analyze the layout of the document and look for structurally similar documents in the database. Unfortunately, both of these approaches require computationally intensive page analysis. One way to reduce the computational analysis is to embed specially designed markers in the documents, that the documents can be reliably identified.
Recently, alternatives to the text based approach have been developed by extracting features directly from images, with the goal of achieving efficiency and robustness over OCR. An example of such a feature is word length. Using sequences of word lengths in documents as indexes, matching documents may be identified by comparing the number of hits in each of the images generated by the query. Another approach is to map alphabetic characters to a small set of character shape codes (CSC's) which can be used to compile search keys for ASCII text retrieval. CSC's can also be obtained from text images based on the relative positions of connected components to baselines and x-height lines. In this way CSC's can be used for word spotting in document images. The application of CSC's has been extended to document duplicate detection by constructing multiple indexes using short sequences of CSC's extracted from the first line of text of sufficient length.
A significant disadvantage of the above-described approaches is that they are inherently text line based. Line, word or even character segmentation must usually be performed. In one non-text-based approach, duplicate detection is based on horizontal projection profiles. The distance between wavelet coefficient vectors of the profiles represents document similarity. This technique may out-perform the text-based approach on degraded documents and documents with small amounts of text.
Because the majority of document images in databases are stored in compressed formats, it is advantageous to perform document matching on compressed files. This eliminates the need for decompression and recompression and makes commercialization more feasible by reducing the amount of memory required. Of course, matching compressed files presents additional challenges. For CCITT Group 4 compressed files, pass codes have been shown to contain information useful for identifying similar documents. In one prior-art technique, pass codes are extracted from a small text region and used with the Hausdorff distance metric to correctly identify a high percentage of duplicate documents. However, calculation of the Hausdorff distance is computationally intensive and the number of distance calculations scales linearly with the size of database.